جست‌وجوی استاد و پژوهشگر

Herman Barkemaمشاهده پروفایل

استاد

• Epidemiology of Infectious Diseases
• Antimicrobial Resistance
• Veterinary Epidemiology
• +۳ مورد دیگر

Herman Barkema is a Professor in Epidemiology of Infectious Diseases at the Faculty of Veterinary Medicine, University of Calgary, with a joint appointment in the Department of Community Health Sciences, Cumming School of Medicine. His research focuses on preventing infectious diseases in cattle herds (e.g., mastitis, Johne’s disease) and antimicrobial resistance, integrating animal and public health perspectives under the One Health framework. He founded the Department of Production Animal Health and leads the AMR – One Health Consortium and CAN-AMR-Net. DVM, Veterinary Medicine, Utrecht University (1988) PhD, Veterinary Science, Utrecht University (1998) His work spans clinical veterinary epidemiology, antimicrobial resistance mechanisms, and zoonotic disease control. He has over 425 publications and collaborates globally, including as a Foreign Expert at China Agricultural University. Recent research trends highlight his focus on multi-omics approaches, ceRNA networks, and antimicrobial stewardship in dairy systems, with significant contributions to Johne’s disease diagnostics and mastitis prevention. Roger Morris ISVEE Award (2024) Plowright Prize (2022) Killam Annual Professorship (2019) Multiple Journal of Dairy Science Most Cited Awards (2011–2024) Fellow, Canadian Academy of Health Sciences (2014) Dr. Barkema supervises graduate students and postdoctoral fellows, emphasizing interdisciplinary mentorship. He co-developed the Veterinary DialogueTrainer for clinical communication training and participates in public engagement initiatives like Breakfast on the Farm. He leads the AMR – One Health Consortium and CAN-AMR-Net, integrating stakeholders across Canada and internationally. His affiliations include the Snyder Institute for Chronic Diseases and strategic initiatives on Infectious Diseases and One Health (2015–2025).

Shoudong Huangمشاهده پروفایل

استاد

• Mobile robot navigation
• Simultaneous localization and mapping (SLAM)
• Underwater robotics
• +۴ مورد دیگر

Shoudong Huang is a Professor at the School of Mechanical and Mechatronic Engineering , University of Technology Sydney, and Deputy Director of the UTS Robotics Institute. His research focuses on mobile robot navigation , SLAM , nonlinear state estimation , and surgical robotics . He has published over 200 papers and is recognized as one of the 100 Most Influential Scholars in Robotics (Aminer, 2018). PhD in Automatic Control, Northeastern University (China) Postdoctoral Research Fellow, University of Hong Kong (1998-2000) Research Fellow, Australian National University (2001-2003) Full-time academic roles at UTS since 2004 His work addresses challenges in robot localization across extreme environments (underwater, underground mining, surgical settings) and develops globally optimal SLAM algorithms with guaranteed performance. He has secured over $4 million AUD in external funding, including ARC Discovery grants and industry partnerships. Recent publications emphasize cross-modal calibration (camera-LiDAR), interval analysis for bounded noise , and template-based deformable surface reconstruction . These span applications in autonomous driving, surgical navigation, and UAV guidance. Chancellor’s Medal for Research Excellence (2020) Supervisor of the Year (2023) Best Paper Award (2016 ICARCV) Huang serves as Associate Editor for IEEE Transactions on Robotics and International Journal of Robotics Research , and has held leadership roles in top robotics conferences like IROS and RSS. His collaborations span MIT, USC, Zhejiang University, and industry partners including PMSW Research Pty Ltd and Multiplex Constructions Pty Ltd.

Omar A. Salehمشاهده پروفایل

استاد

• Biophysics
• Soft Matter Physics
• Polymer Physics
• +۵ مورد دیگر

Professor Omar A. Saleh is a distinguished physicist and materials scientist at the University of California, Santa Barbara, holding appointments in both the Materials and Physics Departments. Since summer 2023, he has served as Chair of the Materials Department and maintains a minority appointment in the Biomolecular Science and Engineering (BMSE) Program, where he previously served as Director from 2013-2017. His educational background includes a B.S. in Physics from MIT (1997) and a Ph.D. in Physics from Princeton (2003), supported by a Hertz Fellowship. Following postdoctoral work at École Normale Supérieure in Paris developing single-molecule techniques for motor protein/DNA studies, he joined UCSB in 2005. Saleh's research centers on fundamental principles of biomolecular behavior through experimental investigation of biopolymer elasticity and biomimetic organelles. His lab pioneers precision single-molecule stretching experiments to study entropic/energetic contributions in soft systems and creates life-like behaviors using reconstituted nucleic acid/protein assemblies. Key focus areas include DNA nanostar phase separation, liquid-liquid phase behavior, intrinsically disordered proteins, and non-equilibrium biomolecular systems. His publication trends reveal a strong emphasis on biomolecular condensates (2023-2025), with recurring themes in DNA nanotechnology, polyelectrolyte physics, and single-molecule mechanics. Recent work explores tension-mediated control of phase separation, transcriptional regulation of biomolecular liquids, and active matter principles in DNA systems. NSF CAREER Award (2008) Bessel Research Award from Alexander von Humboldt Society (2017) Fellow of the American Physical Society (2019) Saleh actively mentors graduate students and postdocs including Sam Wilken, Gabrielle Abraham, Anna Nguyen, and Aria Chaderjian, whose research spans DNA nanostar liquids, active droplets, and complex coacervation. His lab develops innovative instrumentation including high-speed magnetic tweezers and GPU-based tracking systems, supported by grants such as NSF/MCB-BSF: Direct force measurements of intrinsically disordered proteins. The Saleh Group operates at BioE 3006, focusing on creating quantitative models of biological function through physical reconstitution.

Andrew Davisonمشاهده پروفایل

استاد

• Computer Vision
• Robotics
• SLAM
• +۳ مورد دیگر

Professor Andrew Davison holds the position of Professor of Robot Vision at Imperial College London's Department of Computing. He leads the Dyson Robotics Laboratory and the Robot Vision Research Group, focusing on advancing SLAM (Simultaneous Localization and Mapping) and Spatial AI. His groundbreaking work includes the MonoSLAM algorithm (2003), enabling real-time 3D vision for robotics and AR/VR. Current research emphasizes scalable, semantic-rich Spatial AI systems, as outlined in his FutureMapping papers (2018–2019). Education: BA in Physics (Oxford, 1994), D.Phil. (Oxford, 1998). Postdoctoral work at AIST, Japan (1998–2000), followed by a lectureship at Imperial (2002–present). Industrial collaborations include SLAMcore, a Spatial AI startup, and Dyson Robotics Lab. Over 18 PhD students supervised, many now leading roles at Meta, NVIDIA, SLAMcore, and academia. Notable contributions include DTAM, KinectFusion, and Event Camera SLAM. Recognized for software tools like SceneLib and contributions to robotics benchmarks (SLAMBench). Active on Twitter (@AjdDavison) for research updates.

Amitabha Bagchiمشاهده پروفایل

استاد

• Data Algorithmics
• Probability and Networks
• Theoretical Computer Science
• +۷ مورد دیگر

Amitabha Bagchi is a Professor in the Department of Computer Science and Engineering at IIT Delhi. His research spans data algorithmics, probability, networks, and theoretical computer science, with applications in distributed systems, social networks, and AI-driven platforms. He has published extensively in leading venues such as SIGMOD, VLDB, ICDE, AAAI, and KDD, often collaborating with students and researchers on problems involving graph algorithms, fairness, and large-scale data analysis. Research Interests: His primary research interests include Data Algorithmics, Probability and Networks, Theoretical Computer Science, Distributed Algorithms, Graph Algorithms, and Machine Learning Theory. He investigates algorithmic foundations for real-world problems such as food delivery optimization, social network analysis, and efficient data structures for streaming and large graphs. Publication Trends: Recent publications focus on fairness in gig economy platforms, efficient solvers for graph Laplacians, generalization in neural networks, and temporal graph querying. His work combines theoretical rigor with practical impact, often involving GPU acceleration, distributed computing, and data-aware algorithm design. Scientific Service: Editor, Algorithms (2020–present) Editor, Journal of Discrete Algorithms , Elsevier (2006–2018) Guest Editor, special issue on Algorithms for Shortest Paths in Dynamic and Evolving Networks , Algorithms (2021) Volume Editor for proceedings of ESA, ATMOS, COCOON, and others Conference Leadership: He has served on numerous program committees and as chair for conferences including ESA (Engineering Track, 2016), ATMOS (2020), and ICALP (2019). His involvement spans algorithmic engineering, transportation optimization, and theoretical computer science forums. Teaching: He currently teaches COL863: Special Topics in Theoretical Computer Science on concentration inequalities and their applications. He has previously taught advanced courses in algorithms and data structures.

• Integrated Optics
• Silicon Photonics
• Semiconductor Lasers
• +۳ مورد دیگر

Professor Marc Sorel is a faculty member in the Department of Electronic & Nanoscale Engineering at the University of Glasgow's School of Engineering. He holds a PhD from Università di Pavia (1999) and joined the Optoelectronics Research Group at Glasgow in 1998 with a Rotary Foundation fellowship. Appointed Lecturer in 2002 and Senior Lecturer in 2008, he is now a Professor specializing in integrated optics, silicon photonics, and semiconductor lasers. His research focuses on applications like quantum technology, mid-infrared optoelectronics, and nonlinear photonics. Notable projects include silicon nitride optical phased arrays and rubidium-based atomic systems. He leads a team advancing chip-scale sensors and photonic integrated circuits. Collaborations with institutions like the Quantum Technology Hub highlight his industry engagement. Research interests span semiconductor ring lasers, ultrashort pulse lasers, and coupled ring resonators on silicon-on-insulator platforms. His work integrates materials science (e.g., alumina/silicon nitride) with quantum and optical engineering innovations. Over 300 publications and presentations at conferences like CLEO and ECOC reflect his global impact. Current efforts emphasize mid-infrared sensing, cold atom systems, and high-precision laser development. His lab develops photonic components for atomic trapping, quantum communication, and biomedical sensing. Recent advancements include sub-kHz linewidth lasers and low-loss waveguides. Funding from UK Research and Innovation supports his exploration of next-generation photonic technologies.

Rob Timmermansمشاهده پروفایل

استاد

• Theoretical Particle Physics
• Quantum Mechanics
• Quantum Field Theory
• +۳ مورد دیگر

Prof. Rob Timmermans is a Professor of Theoretical Physics and Vice-Dean for Education at the University of Groningen (UG). He is affiliated with the Faculty of Science and Engineering and the Precision Frontier — Van Swinderen Institute for Particle Physics and Gravity. His research focuses on theoretical particle physics, quantum mechanics, and precision measurements, particularly in electric dipole moment (EDM) searches using molecules like BaF. His work includes developing methods for molecular beam manipulation, phase-space analysis, and symmetry violation studies. He has contributed to collaborations such as NL-eEDM, advancing techniques for EDM detection and precision physics. Prof. Timmermans has received nominations for teaching awards, reflecting his commitment to education. Research highlights include studies on nucleon decay, antinucleon-nucleon interactions, and chiral effective field theory. His lab activities involve collaborations on laser-cooled molecules and trapping techniques. Prof. Timmermans’ articles often address fundamental physics questions, such as Lorentz violation in beta decay and parity violation in molecular systems. Awards: Nominated for Faculty Teaching Award 2014, Teacher of the Year 2014-15. Grants/Advising: Leads projects on EDM searches and particle physics, with active roles in international collaborations. Labs/Teams: Van Swinderen Institute, Precision Frontier group.

Elaine Francisمشاهده پروفایل

استاد

• Syntax
• Psycholinguistics
• Relative Clauses
• +۶ مورد دیگر

Elaine Francis is a Professor of English and Linguistics at Purdue University's College of Liberal Arts, where she serves as Associate Head of the Department of English and directs the Experimental Linguistics Lab. She holds additional affiliations as an Affiliate Faculty Member in the Department of Linguistics and a Courtesy Faculty Member in the Department of Speech, Language, and Hearing Sciences. Ph.D. in Linguistics, University of Chicago (1999) B.A. in Linguistics, College of William and Mary (1993) MA in Linguistics, University of Chicago (1995) Former Assistant Professor at the University of Hong Kong (1999-2002) Professor Francis's research focuses on syntax and its interfaces with semantics, discourse information structure, and language processing in production and comprehension. She employs experimental methods to investigate syntactic, semantic, discourse-pragmatic, and cognitive factors underlying complex sentence structures. Her primary research areas include word order alternations, filler-gap dependencies, resumptive pronouns, and grammatical acceptability. She examines how various factors contribute to grammatical alternations in language use and explores how processing pressures in production and comprehension contribute to grammatical conventions. Her recent publication record shows consistent output across multiple linguistic subfields, with a strong emphasis on experimental syntax and psycholinguistics. Her 2022 book, Gradient Acceptability and Linguistic Theory , represents a significant contribution to understanding acceptability judgment tasks in relation to syntactic theory. Her work spans theoretical syntax, experimental linguistics, second language acquisition, and cross-linguistic comparison, often bridging the gap between theoretical frameworks and empirical evidence. Regularly teaches short courses at Linguistic Society of America Linguistic Institutes Member of Linguistic Society of America Ethics Committee Editorial board member of Glossa Psycholinguistics Co-editor of Polymorphous Linguistics: Jim McCawley's Legacy (2005) Co-editor of Mismatch: Form-Function Incongruity and the Architecture of Grammar (2003) Professor Francis actively supervises graduate students in linguistics, though she has indicated she will be unable to take on new graduate students for the 2025-2026 academic year. She has received research support for her experimental work, particularly for investigating syntactic phenomena through controlled experiments and corpus analysis. Her collaborative work extends across multiple institutions and disciplines, including collaborations with researchers in computational linguistics, cognitive science, and clinical linguistics. She directs the Experimental Linguistics Lab at Purdue University, which conducts research on syntactic processing using various experimental methodologies. The lab investigates how speakers produce and comprehend complex sentence structures, with particular attention to how grammatical knowledge interacts with processing constraints. Current projects include research on relative clauses, syntactic islands, and acceptability gradient phenomena across multiple languages.

Silvia Lindtnerمشاهده پروفایل

دانشیار

• Science and Technology Studies (STS)
• Cultural Anthropology
• Feminist Studies
• +۷ مورد دیگر

Silvia Lindtner is an Associate Professor at the University of Michigan's School of Information and Director of the Center for Ethics, Society, and Computing (ESC). She holds courtesy appointments in the Penny W. Stamps School of Art and Design and the Digital Studies Institute. Her work focuses on the social and cultural study of technology in China and globally, with a focus on data-driven governance, AI ethics, and the intersection of technology and labor. Lindtner has conducted nearly two decades of fieldwork in China, examining innovation ecosystems, digital economies, and the affective dimensions of state control. She is the author of the award-winning *Prototype Nation: China and the Contested Promise of Innovation* (2020) and co-author of *Technoprecarious* (2020). Education: PhD in Information and Computer Sciences from University of California, Irvine; BS/MS in Media Technology and Design from University of Hagenberg, Austria. Research Interests: Science and Technology Studies (STS), China Studies, HCI, Critical Computing, AI Ethics, Data Governance, Labor Studies, and Transnational Digital Economies. She explores how technological systems shape governance, labor practices, and everyday life, particularly in contexts of global inequality and state surveillance. Awards: Joseph Levenson Prize (2021), Francis L.K. Hsu Book Prize (2021), ACM Distinguished Member (2022), and fellowships from the National Committee on US-China Relations (2021-2023) and China-US Scholars Program (2021-2022). Grants: Supported by NSF, IMLS, Intel Labs, Google Anita Borg, and Chinese National Natural Science Foundation. Leads interdisciplinary initiatives including Tech.Culture.Matters research group and Michigan Interactive and Social Computing (MISC) research group. Labs/Teams: Directs the Center for Ethics, Society, and Computing (ESC) and collaborates with the Lieberthal-Rogel Center for Chinese Studies. Current projects include studies on affective control in AI governance and transnational data practices in healthcare and labor.

Furong Huangمشاهده پروفایل

دانشیار

• Machine Learning
• Trustworthy Machine Learning
• Generative AI
• +۷ مورد دیگر

Furong Huang is an Associate Professor at the University of Maryland's Department of Computer Science, with affiliations at the Institute for Advanced Computer Studies, Center for Machine Learning, Maryland Robotics Center, and Applied Mathematics, Statistics, and Scientific Computation Program. Her research bridges trustworthy machine learning, sequential decision-making, and foundation models for robotics, emphasizing reliability, interpretability, and ethical standards. Research Interests: Trustworthy AI Generative AI Reinforcement Learning AI Security Algorithmic Fairness Foundation Models for Robotics Recent Publications span leading conferences (NeurIPS, ICML, ICLR, CVPR) and journals, focusing on: Robustness in Vision-Language Systems Trustworthy Generative AI Foundation Models for Sequential Decision-Making AI Security and Watermarking Scientific Awards MIT TR35 Innovator Under 35 (Asia Pacific 2022) Best Paper Award, AdvML Frontier Workshop, NeurIPS 2024 NSF NAIRR Pilot Awardee Microsoft Accelerate Foundation Models Research Award (2023) JP Morgan Faculty Research Awards (2019–2022) Advising and Grants : Her lab has graduated students to roles at OpenAI, Google, Meta, and Netflix. Research funded by DARPA, NSF, ONR, AFOSR, and industry partners like Microsoft, Adobe, and Capital One. Labs & Teams : Leads research groups focused on Trustworthy AI and Robotics at the University of Maryland, collaborating with the Maryland Robotics Center and Applied Mathematics Program.

Huan Zhangمشاهده پروفایل

استادیار

• Trustworthy Machine Learning
• Formal Verification of Machine Learning
• AI Safety
• +۴ مورد دیگر

Huan Zhang serves as an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Illinois Urbana-Champaign (UIUC), with affiliate appointments in the Department of Computer Science and the Coordinated Science Laboratory. His research focuses on building trustworthy AI systems with formal verification techniques to provide provable guarantees for safety-critical applications, particularly in machine learning and neural networks. Dr. Zhang received his Ph.D. in Computer Science from UCLA in 2020, advised by Professor Cho-Jui Hsieh. His academic journey includes an M.S. in Computer Engineering from UC Davis (2014) and a Bachelor of Engineering from Zhejiang University (2012). Prior to joining UIUC, he completed a postdoctoral fellowship at Carnegie Mellon University (2021-2023) with Professor Zico Kolter. Huan Zhang's research program centers on formal verification of machine learning systems, with particular emphasis on neural network verification, AI safety, robustness, and reliability. He pioneered the linear bound propagation-based verification framework that enables formal verification for networks with millions of neurons. His work spans five major research categories: formal verification of machine learning, training trustworthy ML models, machine learning safety and adversarial attacks, reinforcement learning safety, and optimization for scalable machine learning. His CROWN framework (NeurIPS 2018) established a foundational approach for neural network verification through efficient linear bound propagation. His recent publications demonstrate a strategic expansion from foundational verification techniques toward increasingly complex systems including large language models, vision-language models, and robotic control systems. The research trajectory shows a clear progression from theoretical frameworks to practical implementations with real-world applications, particularly in safety-critical domains. His work increasingly bridges formal methods with practical AI deployment requirements. Winner of International Verification of Neural Networks Competition (VNN-COMP) as team leader (2021-2024) Schmidt Futures AI2050 Early Career Fellowship ($300,000 research grant) Adversarial Machine Learning (AdvML) Rising Star Award (2021) IBM PhD Fellowship (2018) Dr. Zhang leads the development of α,β-CROWN, a neural network verifier that has won VNN-COMP 2021-2023, and auto_LiRPA, a PyTorch-based library for perturbation analysis on general computational graphs. He has mentored numerous graduate students from CMU, UCLA, UIUC, and Columbia University. His research is supported by significant funding including the Schmidt Futures fellowship and industry collaborations. He teaches courses including ECE 120, ECE 484, ECE 584, and ECE 598 HZ on topics ranging from computing fundamentals to safe autonomy and machine learning. Dr. Zhang maintains active research collaborations across multiple institutions and is affiliated with UIUC's Coordinated Science Laboratory. His work has significant implications for safety-critical AI applications in autonomous systems, healthcare, and other mission-critical domains where reliability guarantees are essential. He regularly gives guest lectures at institutions including Yale, Stony Brook, and the University of Nebraska Lincoln on formal verification techniques.

• Machine Learning
• Natural Language Processing
• Artificial Intelligence
• +۳ مورد دیگر

Prof. Luke Zettlemoyer is an Adjunct Professor of Computer Science and Engineering at the University of Washington, with affiliations to the Department of Linguistics. He focuses on machine learning, natural language processing, and multimodal systems, contributing to advancements in large language models, ethical AI, and scalable architectures. His research addresses challenges in model alignment, generalization, and cross-domain integration. Key research interests include multimodal reward models, efficient tokenization strategies, and model optimization techniques. He has explored topics such as neural trajectories for robot learning, content-adaptive image processing, and ethical mitigation of verbatim data reproduction. His publications span 2023–2025, emphasizing practical applications of AI in robotics, vision-language systems, and scalable retrieval-based models. While no formal awards are listed, his work reflects significant contributions to foundational AI research.

Andrea Cavalleriمشاهده پروفایل

استاد

• Magnetism
• Non-equilibrium phenomena
• Complex correlated materials
• +۶ مورد دیگر

Andrea Cavalleri is a renowned physicist affiliated with both the University of Hamburg and the University of Oxford as a Professor of Physics. He serves as Founding Director of the Max Planck Institute for the Structure and Dynamics of Matter since 2013, having previously held leadership roles at the same institute and its predecessor departments. Laurea and PhD in Physics, University of Pavia (1994–1998) Postdoc, University of California, San Diego (1998–2001) Scientific staff, Lawrence Berkeley National Laboratory (2001–2005) His research focuses on ultrafast science, superconductivity, and nonlinear phononics. He pioneered femtosecond x-ray experiments to study atomic-structural dynamics in solids and demonstrated light-induced superconductivity in cuprates and fullerites. Current work involves X-ray Free Electron Lasers for photo-induced phase transitions. Selected publications highlight trends in ultrafast control of condensed matter phases, including superconducting plasma waves, Josephson solitons, and Dirac carrier dynamics in graphene. His work bridges experimental techniques with fundamental insights into quantum materials. Fellow of the American Physical Society (2011), Institute of Physics (2015), and AAAS (2016) Max Born Medal (2015), Dannie Heinemann Prize (2015), ERC Synergy Grant (2013) David Shirley Award (2004) and European Young Investigator Award (2004) Cavalleri's research has driven the development of tools for studying non-equilibrium phenomena in complex solids, enabling new directions in materials science and quantum physics. He has held named lectureships at institutions like Collège de France and Uppsala University.

Chris Donahueمشاهده پروفایل

استادیار

• Artificial Intelligence
• Machine Learning
• Music Technology
• +۵ مورد دیگر

Chris Donahue is an Assistant Professor in the Computer Science Department at Carnegie Mellon University . He also serves as a part-time Research Scientist at Google DeepMind on the Magenta team. His work focuses on leveraging generative AI to enhance human creativity, particularly in music. Education: PhD in Computer Science (UC San Diego), Postdoctoral Scholar (Stanford University) His research spans controllable generative modeling of music and audio , with a focus on real-time interactive systems. Projects like Piano Genie , Beat Sage , and Copilot Arena demonstrate his commitment to real-world deployment. His Generative Creativity Lab (G-CLef) explores AI applications beyond music, including programming and natural language. Recent publications highlight advancements in multimodal music evaluation , real-time adaptation , and AI-driven sound morphing . He co-developed Magenta RealTime , an open-weight real-time music generation model, and MusicFX DJ Mode . Scientific Awards: Best Paper Award (top 1) at NAACL Student Research Workshop 2025 Best Paper Award (top 1% of submissions) at CHI 2025 Best Paper Runner-up at ISMIR 2021 He co-advises PhD students like Wayne Chi (NDSEG Fellow) and mentors Irmak Bukey . His lab receives support from the AIxArts incubator fund at CMU .

Kenny S Breuerمشاهده پروفایل

استاد

• Fluid Mechanics
• Animal Flight
• Bacterial Motility
• +۴ مورد دیگر

Kenneth S. Breuer is a Professor of Engineering at Brown University, serving as Director of the Center for Fluid Mechanics. He holds appointments in the School of Engineering and collaborates across disciplines, including Biology and Physics. His research focuses on fluid mechanics, animal flight mechanics (particularly bats), bacterial motility, renewable energy, and turbulence. Breuer earned his Sc.B. from Brown University and M.Sc./Ph.D. from MIT, returning to Brown in 1999 after faculty service at MIT. Education: Sc.B. (Brown), M.Sc./Ph.D. (MIT). Awards include Fellowships from the American Physical Society and American Society of Mechanical Engineers, and the Harold and Esther Edgerton Chair at MIT. He has authored over 100 publications and edited books such as *Microscale Diagnostic Techniques*. Research Interests: Fluid mechanics at micro/nanoscales, bio-inspired flight mechanisms, energy harvesting, and vortex dynamics. Collaborations include Professors Sharon Swartz (Biology) and Thomas Powers (Engineering). Current projects explore bat wing aerodynamics, membrane hydrofoils, and aerosol transmission in vehicles. Awards: APS Division of Fluid Dynamics Chair (2012), Midwest Mechanics Lecturer, and multiple fellowships. Teaching includes courses in Fluid Mechanics, Transport Phenomena, and Renewable Energy Systems. His lab develops bio-inspired robotic systems and studies flow interactions in animal and engineered systems.